1. Field of the Invention
The present invention generally relates to medical devices that measure cardiac inter-beat intervals, analyze the said cardiac inter-beat intervals, and classify the underlying cardiac rhythm based on the said cardiac inter-beat intervals. More particularly, the present invention relates to a method and apparatus for accurate detection of atrial fibrillation based on analysis of the cardiac inter-beat intervals.
2. Description of the Related Art
The variation of cardiac inter-beat (e.g., PP, RR) intervals results from both rhythmic activity of the heart electrical source and the dynamic properties of the cardiac conduction pathway, both of which are under autonomic control. In normal sinus rhythm, the RR intervals are known to fluctuate at various time scales, a phenomenon known as heart rate variability (HRV), which has been extensively investigated to probe the autonomic nervous activity. On the other hand, structural or functional abnormalities of the cardiac electrical conduction system can lead to cardiac arrhythmias.
The RR interval is a preferred choice to represent cardiac inter-beat interval due to easy acquisition of the electrocardiogram (ECG) signals, and the prominent QRS complexes present in these signals. The RR intervals not only can be easily measured from the surface ECG, but also can be measured from the subcutaneous ECG that is recorded by placing electrodes under the skin, or from the intracardiac electrogram (IEGM) that is recorded by inserting electrodes into the heart. Alternatively, the cardiac inter-beat intervals can also be obtained from other types of biosignals that are known to show the same rhythmic variation as the cardiac beats, including but not limited to, the blood pressure signal, the transthoracic impedance signal, the pulse oximeter signal, finger plethysmography signal, etc.
Cardiac rhythm classification based on time series analysis of RR intervals has been a research thrust during the past decades, with particular focus on automatic detection of atrial fibrillation (AF). AF remains the most common clinical tachyarrhythmia that causes significant morbidity and mortality. The clinical hallmark of AF is an irregularly irregular ventricular rhythm, which is sometimes characterized as random RR intervals. Converging evidence suggests that the irregular ventricular rhythm in AF had adverse hemodynamic effects independent of the fast ventricular rate. Moreover, it is well known that AF patients have significantly higher risk of thromboembolic stroke. Presence of AF is also a strong risk factor for developing other serious, chronic heart diseases, such as dilated cardiomyopathy and congestive heart failure (HF). In addition, clinical management of AF patients requires accurate assessment of the antiarrhythmic efficacy of various therapies, e.g., drug-based rhythm control, internal or external cardioversion, catheter ablation, etc. Therefore, early detection of AF and continuous monitoring of AF burden is critically important in cardiac rhythm management.
Numerous techniques have been developed to automatically classify cardiac rhythms, in particular AF, by means of RR interval analysis. Nonetheless, all existing methods have various limitations. One typical approach for AF detection is to collect multiple beats of RR intervals and quantify their global or beat-to-beat variability by means of statistical metrics, such as mean, median, absolute deviation, etc. Representative prior arts include U.S. Pat. No. 6,922,584 issued to Wang et al., U.S. Pat. No. 6,937,887 issued to Bock, and U.S. Pat. Appl. No. 2006/0084883 by Linker. However, the specificity for AF detection using these simple variability metrics is not sufficiently high, because many other types of cardiac rhythms (e.g., exercise, frequent extra-systoles, etc.) also show large variability of RR intervals.
Some non-linear complexity measures that have been widely used in HRV analysis, e.g., sample entropy, asymmetry index, symbolic coding, etc., have also been suggested for cardiac rhythm classification, as disclosed in U.S. Pub. No. 2007/0066906 by Goldberger et al. However, these measures only reflect the global complexity of the RR intervals, whereas it is known that many types of cardiac rhythms have overlapping range of RR interval complexity, for example, AF vs. ventricular fibrillation (VF), sinus rhythm with depressed HRV (e.g., in HF patients) vs. stable sinus tachycardia, and so on.
Another non-linear approach for cardiac rhythm classification is based on the Lorenz plots, which have also been widely used in HRV analysis for the past two decades. By plotting the scatter plot of each RR interval vs. the immediately preceding RR interval, a 2D Lorenz plot is obtained which shows characteristic clusters for different types of cardiac rhythms. Similarly, 3D or higher dimensional Lorenz plot could be obtained by embedding more dimensions with more beat delays. Representative prior arts include U.S. Pat. No. 7,353,057 issued to Schiessle et al., and U.S. Pat. Pub. No. 2005/0171447. Obviously, these conventional Lorenz plots focus only on RR intervals (or heart rate) whereas the information on directional change of RR intervals (or change of heart rate) is hidden. On the other hand, cardiac rhythm classification based on Lorenz plots of dRR intervals (i.e., the difference between two adjacent RR intervals) has also been disclosed in U.S. Pat. No. 7,031,765 issued to Ritscher et al., and U.S. Pat. Pub. No. 2006/0247548 by Sarkar et al. However, by focusing on the beat-to-beat change of the RR intervals (or change of heart rate), these dRR Lorenz plots ignore the information pertaining to the raw RR intervals (or heart rate).